{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "sns.set(rc={\"figure.figsize\": (8, 6)})\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]#设置全局变量字体为黑体\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False#设置全局变量正常显示负号\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_csv = pd.read_excel(r'./使用数据/属性表.xls')\n",
    "df = pd.DataFrame(file_csv)\n",
    "df = df.dropna(subset=['起源','地类','平均胸径','平均树高'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'geometry',\n",
       " '调查方式',\n",
       " '省',\n",
       " '市',\n",
       " '县',\n",
       " '行政乡',\n",
       " '行政村',\n",
       " '经营单位',\n",
       " '乡镇场',\n",
       " '村分场',\n",
       " '小班号',\n",
       " '细班号',\n",
       " '流域名称',\n",
       " '地貌类型',\n",
       " '平均海拔高',\n",
       " '坡向',\n",
       " '坡位',\n",
       " '坡度',\n",
       " '成土母岩',\n",
       " '土壤名称',\n",
       " '土层厚度',\n",
       " '腐殖质厚度',\n",
       " '植被总盖度',\n",
       " '水土流失类型',\n",
       " '水土流失强度',\n",
       " '土地所有权',\n",
       " '林木所有权',\n",
       " '林木使用权',\n",
       " '面积亩',\n",
       " '地类',\n",
       " '林种',\n",
       " '经营类型',\n",
       " '起源',\n",
       " '优势树种',\n",
       " '平均年龄',\n",
       " '龄组',\n",
       " '多目标经营措施',\n",
       " '轮封',\n",
       " '平均胸径',\n",
       " '平均树高',\n",
       " '郁闭度',\n",
       " '活立木总蓄积',\n",
       " '平差后活立木总蓄积',\n",
       " '林分蓄积',\n",
       " '平差后林分蓄积',\n",
       " '散生蓄积',\n",
       " '平差后散生蓄积',\n",
       " '四旁树蓄积',\n",
       " '平差后四旁树蓄积',\n",
       " '四旁树株数',\n",
       " '杉类占',\n",
       " '松类占',\n",
       " '硬阔类占',\n",
       " '软阔类占',\n",
       " '树种比',\n",
       " '枯立木蓄积',\n",
       " '毛竹株数',\n",
       " '幼龄毛竹株数',\n",
       " '壮龄毛竹株数',\n",
       " '杂竹株数',\n",
       " '工程类别',\n",
       " '造林类型',\n",
       " '公益林事权',\n",
       " '公益林区位类型',\n",
       " '公益林区域名称',\n",
       " '群落结构类型',\n",
       " '林层结构',\n",
       " '树种结构',\n",
       " '病虫害等级',\n",
       " '火灾等级',\n",
       " '其它灾害等级',\n",
       " '森林健康等级',\n",
       " '森林自然度',\n",
       " '人工林生长等级',\n",
       " '天然更新等级',\n",
       " '用材林可及度',\n",
       " '经营措施类型',\n",
       " '大等于5CM亩均株数',\n",
       " '小于5CM亩均株数',\n",
       " '亩平均蓄积',\n",
       " '散生木株数',\n",
       " '宗地号',\n",
       " '森林类别',\n",
       " '交通区位等级',\n",
       " '林地质量等级',\n",
       " '林地保护等级',\n",
       " '土地退化类型',\n",
       " '国家级公益林保护等级',\n",
       " '林带长度',\n",
       " '林带宽度',\n",
       " '林地功能分区',\n",
       " '主体功能区',\n",
       " '变化原因',\n",
       " '变化年度',\n",
       " '变更依据',\n",
       " '土地管理类型',\n",
       " '插花县代码',\n",
       " '公益林关键字',\n",
       " '小班唯一编号',\n",
       " '土地利用现状地类',\n",
       " '经营界限',\n",
       " '生态红线',\n",
       " '说明',\n",
       " '林木生长势',\n",
       " '灌木覆盖度',\n",
       " '灌木平均高',\n",
       " '其它',\n",
       " '重要生态功能区',\n",
       " '土地使用权',\n",
       " '林权权利人',\n",
       " '国家级公益林原保护等级',\n",
       " '是否天保工程区',\n",
       " '是否退耕还林',\n",
       " '新一轮退耕还林年度',\n",
       " '生态区位名称',\n",
       " '界定书编号',\n",
       " '管护协议',\n",
       " '是否符合区划范围标准',\n",
       " '是否已改变林地用途',\n",
       " '处理意向',\n",
       " '自然保护区',\n",
       " '遗产地',\n",
       " '湿地及水库',\n",
       " '五河及源头',\n",
       " '饮用水源',\n",
       " '国家森林公园',\n",
       " '军事禁区',\n",
       " '是否为补充林地',\n",
       " '调查人员',\n",
       " '调查时间',\n",
       " '其他1',\n",
       " '其他2',\n",
       " '调查状态',\n",
       " '评定状态',\n",
       " '天保关键字',\n",
       " '连接字段',\n",
       " '功能区划',\n",
       " '经营区划',\n",
       " '经营类型.1',\n",
       " '生态隔离带']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([12., nan, 11., 13., 33., 31., 15., 34., 23., 32., 22.]),\n",
       " array([ 112,  141,  111, 1311,  113], dtype=int64))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['龄组'].unique(),df['地类'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.pivot_table('面积亩','起源','地类'))\n",
    "plt.savefig(r'G:\\python林业资源\\出图/sns热力图.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"地类\", y=\"面积亩\", hue=\"起源\", data=df)\n",
    "plt.savefig(r'G:\\python林业资源\\出图/sns柱状图.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LkcHzwNeNMfcBG4HvGGO+Bmyz1r5wtnaR5d8GfmGM2Uq4c/kPrLU/PrWSMeYe4Ii19slZ700asy091JX78GbFdIFXzPzjAXY3dcxqG9deUsNTW49gW3owtcnv3BaRxJv2yCDy7X4z8Crwp9ba+621/3BGEERtF1n+BPBZYDtwmbV28Iz17lnol5X6xwO8f6I/Ja4iiubqtdUU52Xy8HNNuhuayDkqpq+h1lo/8LOZtrPWNgKNcVe3QBxq7WMiEOK82oJklxJVhsfFp69Zzr/+8j1e2NnKlnU1068kImlFI5BTwP6WXpwOR8L7CxJp7fISVi0u4vFX36d3UJeaipxrFAYpwLb0UFfhIzszsf0FieRwOPjMtcuZCAR59LcHkl2OiCSYwiDJ/OMB3j/ej0nRU0STlRd5ufWKBt7a385b+9uTXY6IJJDCIMkOtvYRCIY4L02u0rnp8loaKn089Iylb2gs2eWISIIoDJJsf3NPpL8gP9mlxMTldPKFm1cyOhbgR7/ZTygU3+A/EUlNCoMk29/SQ0NlavcXnKmqJIfbr1rMOwc6eWFn6/QriEjKUxgk0ejYBEdODKTlQK4tl9ZwwZJifvz8AQ6f6E92OSIySwqDJDp4LNJfUFeQ7FLi5nQ4+OItKynIzeC7j+9hcGQ82SWJyCwoDJJof0svLqeDZdUFyS5lRnKzPfzRJ1bTM+Dne0/s0ehkkTSmMEiicH9BHpkZrmSXMmOLq/L43I3nse9IDw8/26QOZZE0lT69lueYEX+4v+Cm9bXTN06yUzOfTmXN8lK2dAzy7PajlJc0ctUFlR9pk+lx49ZXD5GUpTBIkgPH+giG0mN8QSwzn5YXZlNX4eOnzx/gePsAS6pPv1R23Ypy3Gl0xZTIQqO/ziTZ39KDy+n4yIdmunI4HFx5QQWBYIite07icTupLU+/u0SJLFQ6cE8S29LDkqo8Mj3p219wJpfTyY0b6inOy+KV3Sdo7RhKdkkiEiOFQRIMj05w5GR6ji+YTobbxeaLF5Gfk8GLO1s51j44/UoiknQKgyQ4cKyXUIiUvZnNbGVluNiyroZCXyYvvdNKS1t63UdWZCFSGCTB/pYe3C4nS6vzkl3KnMnMcHHdukUU52fx8q7jvG01y6lIKlMYJMH+5l6WVOXhcZ87/QXRZHhcXHtJDWUF2fy/X+9n654TyS5JRKagMJhnw6PjtLQNnLOniM7kcTvZfPEili0q4MGnGnlux9FklyQiUSgM5pk92ksIUvZ+x3PB43byh59YxUXLS3n0twf42UuHNFJZJMUoDOaZbenF43ayuOrcGF8QK4/byR9/YhWb1lTx9BvN/OBXjZrLSCSFaNDZPGts7mFpdT6eBTg3g9Pp4M7rDQW5mTz+2mEGRsb5o4+vSuu5mUTOFQvvEymJ+gb9HG0fZGX9wugviMbhcHDblQ189gbDe+93ce+jO+kb9Ce7LJEFT2Ewj/Yd6QFgVUNxkitJvk1rqvmTT66mtXOIb/7obY51aHCaSDIpDObRnsPd5GZ7qCnPTXYpKeGi5aV89TNrmQgG+ceH3mbP+13JLklkwVIYzJNQKMTeI92c31CE0+FIdjkpo74ij7/97CWUFmTzf376Li/uPJbskkQWJIXBPDnWMUT/0Bjn1xclu5SUU5SXxVc/s5ZVi4t46Nkm/uPZJl1pJDLPdDXRPNlzOHwK5PyGhRkG090gB+ALt6zk8Vff5/mdxzh8sp/P37yC/NxMQDfHEZlrCoN5svdwN9WlORT6MpNdSlLEcoMcgJqyXDZeUMm2vSf5hx/t4GNrqikrzNbNcUTmmL5rzQP/eICmo306RRSjhqo8bry8DrfLyTPbW9jf3KMRyyJzLKavWsaYu4E7gHbgLmttW6ztjDE1wGORJg9Za+83xviAR4BMoBD4krV216z2JIXZlh4mAkFWLdBTRDNR6Mvk5vV1vPbuCbY3tjM2EeTzN60gN9uT7NJEzknTHhkYYzYAtwHrgHuBb8TZ7oHI4w3A7caYWuBO4GFr7Rbg74F7ZrUXKW7XgU4yM1zn5M1s5lKGx8XVa6tZu7yE9w518bcPvqnLT0XmSCxHBluAR621AWPMi8B9sbYzxriANdbapwGMMc8Cm6y1909arww4Hk/RxcUzu06/tHT+78kbCoV473A3a00ZVZXxz0fk9Wbiy82a8et7PO5ZrT+TbURrO5s61l9Qza0bl/J/f7WX//XYbm6+ooHP3bKSrIzE9SEk43djtlTz3Eu3emHmNcfy1+QDdgJYa0PGmJw42nmB1klteoGqU0+MMSXAl4Eb4im6q2uQYDC+c8ilpT46Oub/jlvNJwfo6htlZW1B3K9fWupjeNjPwODojF9/fHxiVuvHuw1fblbUtrOt47yafL72e2v5+cvv86vXD/N2Yxt3Xm9YkYCpwJP1uzEbqnnupVu9MHXNTqdj2i/RsXQg9wOTA2Cq23NFazdMuF/gFB/gADDGeIBHga9aa1tiqCMtvXOgAweweommoJgtj9vFp69Zxlc+vYaJQJBvPfoO3318D939sws7EYktDLYBmwGMMcuAzljbWWsDQHekExlgLXA4cvroEeAJa+2Ts6g/5e0+2MWS6nzyvBnJLuWcsbK+iG9+8TI+cWUDuw528jfff4Onth5hfEID1URmKpbTRM8DXzfG3AdsBL5jjPkasM1a+8LZ2kWWfxv4hTFmK+HO5T8APg/cAlQaYz4NNFtrP5OQPUohPQN+mtsG+J1NS5JdStqLNmjtmnU1rFleyi9feZ9fvPI+L+86zvWX1XLpijJcro9+z9HANZGpTRsGkQ7hzcCtwGPW2tfjaWetfcIY00T4qOAea+0g8P3Iv3Pa7oPhg6gLl5YkuZL0d7ZBaxcuLaa0IIudTR088lwTT752mFWLi1hSnY/L+eE8UBq4JjK1mP4yrLV+4GczbWetbQQa464uze2w7ZQVZlNV7E12Kee8qpIcKou9tHYMsftQF2/sbeO9Q12sbChiSVUeGR7dQEfkbPQ1aY70DY3R2NzDzevrcWiW0nnhcDhYVJZLdWkOxzuHefdQJ281tvNOUwf1lXmUF3lZobEeIlEpDObIjv3thEJw2YqyZJey4DgcDqpLc6guzaGzb5Smo70cOdHPtx55h4ZKH1esruRiU0Z+jjr1RU5RGMyRNxvbWFSaQ3WpbmSTTCX5WZTkV3CJKWUiGGLbnpM8/GwT//FcE6amgHXnlXHdhoZ5q2ciCP7xs8/eejbqBJe5ojCYA119oxw81sftVy1OdikSkeFxccWKcm68tJbWziF27G/nrf3tPPRsEw8/10R9hY+V9UWsrC9iaXU+njn6xPWPT/BWY9SpvWKiTnCZK/qtmgPb94f/2C/VKaKU43A4WFSay6LSXD5+ZQOtnUPsP9rH9n0n+fUbLfxqWzMZbifLagpYXJlHfaWPhso8CnIX5tTjsnAoDObAm/vaaKj0UVaoq4hSSbSxCoV5WVyzLp/155cz4p/g4LE+bEsPB4718dSRI5yaOTs/J4O6Ch9VxTlUFHupKPJSWezFp8GEco5QGCRY88kBWtoG+d1rliW7FDnDVGMVzpxPqa7CR12Fj/GJID0Do3T1+enqH6Wjd4R9R3pOuyVnTpabskIv5UXZlBd6KS/Mprwo/F9vlqbblvShMEiwl3e14nE72bC6ItmlyCx53E7KCr0fHOGtW1FOtsdFZ/8oJ7uGOdE1RFvPCG3dwzQd7eWNvaf3BeRmeygvyqai0Et9ZR7LFuVTkDe7GWRF5orCIIFG/BNs29fGpSvKyNG3wnOS0+mgrCCbsoJsLjhj8sGx8QDtvSO0dY/Q3jNMW88wbd0jvHe4m9f3nAQg0+OiMC+TsoJsFpXlUJyXpXEokhIUBgn0xr42/GMBNl1UnexSJAkyPK4POqcnC4VC4SvMWvtobOll7+Eu3jvUxbuHusjOdFFX7mNxVR7F+QoGSR6FQYKEQiFeeqeV2rJcFldONcu3LEQOh4OSgmxKCrJZvbSEtxp9jI4FON45SEvbIE3H+tjf0kt+TgamroAlVXN3aavIVBQGCXLoeD9H2wf57PVG3+5kWlkZLhZX5bO4Kp+x8QDNbQM0tfSxfV87u5o6WVZTgKkt0D2fZd4oDBLk6W3N5GS5uWxlebJLkTkS7dLUeEW7QV+Gx8WyRQUsrc6no3eUxuYe9h3uZt+RbuorfFywpIT8XF3CKnNLYZAAzScH2HWwk09sbCBbo0PPWWebRjtWFy4vnfJnDoeDssJsygqzGRwZZ39zT2RepQEWV+VxwVLdLU/mjj65EuA/tx4hO9PNtRcvSnYpco7IzfZwyXllnN9QxN7D3diWXt4/0c/J7hFu37g4LW/ULqlNYTBLR9sH2dnUwW1X1GuQkSRcdqabS84rY2V9Ee+938VbjW281djGDZfXs/miKk2TIQmjMJilJ147TFaGi+vW1UzfWGSGvJH+qM9sWc7zO47x621HePbNZjZfvIgbL6vVtBgya7p+bRbePdTFzqYObry8ToPMZF4U+rK464bz+O7d13CxKeOZN1u4+3vbePzV9xkenV3ntixsOjKYobHxAA8/a6ks9nLDpbXJLkcWmMqSHL5060puWl/HE68d5snXj/D828e47pIaNq2tJk9HChInhcEMPbXtCJ19o/zV716kAUKSNNUlOfzxJ1bR0jbA468e5vHXDvPUtmbWn1/OdetqPjIaWmQqCoMZOHKyn1+/0cL68ys4r0731JXkqy338We/cwEnuoZ4bscxtr53glffPcHK+kI2ranmwqUl+tIiZ6UwiFP/8Bj/+ov3yM/N4NPXLE12OSKnqSzO4bPXG26/ajEv72rlhZ2t3P/4HnKy3FxsyrjYlHJebaGCQT5CYRCHQDDIvz2xl76hcf7mzrW6gkNSVm62h5vX13PjZXXsa+5m656TvNnYxiu7j5OVER7xvLwmn+U1BdRX5CkcRGEQq2AoxEPPNNHY3MPnb1pBfYUmo5PU53Q6WNVQzKqGYsYnAjQ297DrYBdNR3v5+ctdQPi+DfUVPqpLcqgszqGyxEtVcQ6FvkzNs7WAKAxiEAgG+cGvGtm2t41bNtRx5QWVyS5JJG4et4sLlpRwwZISAAaGxzhwrI+mo+HRzW/tb2do0uWpHreTQl8mRb5MCn1ZFOV9+LjQl0mhL5NcrwenAuOcoDCYxtDoOA8+1ciug53cftVibtlQn+ySZAE7NVleqHuY4RlOmudxuxmfmMDpcmLqCjGRiyBCoRADw+O0dQ9zsnuYjt4R+gbH6Bnws7+lh75B/0cm2nM5HRTkhoOhIBIcp56fWlaYm4HH7ZrtrsscUxicxb4j3Tz4q0b6h8b4zHXLuUZzD0mSnZos78z7NsfjwuWl0064l5XhoqYsl5qyD5cFQyFG/QGGR8epKsult99P7+AYvYN++gb9NJ8c4N2DnYxNBD+yvdxsD0V5WeTnZlDoy6QkP5vS/PDRRnFkucupfotkUhhE0do5xH++fpjtje1UFHn5mzsvpkE3rJEFzulw4M1y481ys6K+iN1NHXiz3FSVeD9oEwqFGJ8IMjw6wbB/Ivzf0XGGRifwTwQ52jbA/uYexs8IDKfDQYEvg6JTp6PysijyhYOiKC+8LDfboz6MOaQwiPCPB9h9sJM39rax+2AnGRkubl5fxy0b6sn06BBXJBYOh4MMj4sMj4sC3+mT6E0+mlm9tISevlG6B/z0DvjpGRilZ8BPz4Cf94/3s7Opg4nA6eekTvVhFOSG+yoKcjLJ83rweT34vBnkZocfe7M8ZGW4yMxwqT8jDjGFgTHmbuAOoB24y1rbFms7Y0wN8FikyUPW2vvj2eZcGJ8I0N4zwomuYVraBzl4rJf3j/czNhGkIDeDm9bXsWVdjS4dFZkjTqeDYx2DAGR4nJQXeSkvOv0IY3QswNDoOEMj4SOMocgRRnf/KCe6hpgIBBnxB6Z8DQeQlekmO9NFdoab7Ew3WZHHWRkuMj3hwMhwO8mMBNiH/3VSNjDGyJCfDM/pP3e7HOfkEcq0YWCM2QDcBqwDPgZ8A/j9ONo9EHn8a+A5Y8xTwKJYthmFC8K/SPHq7B3hX37xHt39o6d1vDkdDqpKcrj5inpW1RfRUJk3o+3PFbfLOaupsWe7frzbyM50E5j4aNtU3o+pap7PGuJdP9aa56KGmW5jcs2xrJ+TDcX52VP+/MLlpUyMBxj2TzDin2BwZJzh0XH840H8YxP4x4L4xwP4xwOMjgXCy8aDDPnH6R7wMz4RZHwiQCDa7efOwulw4HE7yfC48LjDR0IuhxO3y4HT5cDtdOByOXE5HTid4eUuZ/if2+nE6Tr988Vx2uMPnwVCIQKBIOOBIIFgiGAwxNUXVVNVknP2+qJ8fk1aNuVpDkcodPY3whhzD9Bprf0XY4wDeNdauzqWdsAa4Li1tjzS5q+Ak8DiWLYZxZXAqzG0ExGRj9oIvBbtB7GcJvIBOwGstSFjzFSxFK2dF2id1KYXqIpjm2d6i/DOnACmPj4UEZHJXEAl4c/QqGIJg35g8of1VJfVRGs3DEzuRfIRPiqKdZtn8jNFqomIyFkdOtsPY7mwdxuwGcAYswzojLWdtTYAdEc6kQHWAofj2KaIiMyDWPoMXMArwA7Cp2geAAqBbdbaF87Wzlp7vzHm48D/ALYCNxIOhJFobRO7ayIiEqtpwwDAGJMJ3AqcsNa+Hm87Y8wKwiHwtLW2J55tiojI3IspDERE5NymyUBERERhICIiCgMREUET1YmkLGNMLnAF0G6tfSfZ9ci57ZztQDbGbAbuttZeP2lZzBPpJVsyJ/KLhTHGAzwB3GutfSkVJyScVKsPeITwAMhC4EtAV6rWG6kjA3iB8JxeH4v892ekcM2nGGN+DPwGeJ4Ur9cYsy9SB8DLhC+dT+maI/V8DrjSWvvFRP3tnZNHBsaYTwJ/wqRJmeKZSM9a2zLvRU8S6+SAyWKMcRMOgtpJixM5IWGi3Qk8bK39iTHmVuAeIDuF6wVYCXzLWvuEMeZJ4FvADaR2zRhjPhWp5zek9u8ExphFwEFr7W2Tlj1DCtccqXEx8GXCR42QoPf5XO0zeAf4whnLtgCPRkZFvwisjwyUW2OtfdpaGwKeBTbNa6XRfaTWJNcTze8THjTIWd7HlNgPa+391tqfRJ6WEZ7bKmXrBbDW7ooEwXnA3wIPpXrNxpgK4C+B7xL+IpbS9QJXARcaY14zxmw1xlxGitdsjHECDwN7gc8aY4pJUM1pHwbGmPuNMS9N/gcMRWnqA1ogPDke4bmRpppIL9mi1ZoyrLUT1tpjkxadbULClNkPY0wJ4W9U/0Qa1BtxNbAcGCT1a/4e8OfAAOnxO7EPuMFaeyXwVdLj9+JOIAj8BbAb2E6Cak7700TW2j+OtjzKTKjxTKSXbDOdyC9ZEj0hYcJF+jgeJfxHf4wUr/cUa+13jTG/JXxOePLdllKqZmPMF4BGa+1rxphrSYPfCcBaa0cij3cBq/iw/wBSs+Z1hKfvOQ4cN8Z0Ez6leMqMa077I4M4xDORXrKl1UR+qT4hYeQ01iPAE9baJ1O93sjrf8EY80+Rp8WRWlK55k8CGyNH5p8jfLpoLIXrBbjfGHNd5PHvEP6WncrvMYSPZlZEaikn3DewLxE1n8tXE9UDP7TWboo8j3kiPWvtYFKKjpiq1mTWFI0x5oeE3+OXUnlCQmPMl4DvAG9HFjUT/qadkvXCB3N3/YjwH/so8N8Ak8o1nxK50dURoIcUrtcYU0f4aNFH+GjxDwnfkCuVa84Gvk/4BmFFwDcJn5abdc3nbBhEE89EesmWjhP5pduEhOlWL6RfzelWLyzcmhdUGIiISHQLqc9ARESmoDAQIXz9duSKo+naeYwxjknPFxlj/uvkZTG+3v+MDHoSSQk6TSQCGGMuJjywa2zS4vOB/UBg0rIMwtNZ/D1QF1n2CPA68G3C9+mG8KCrF6y1X46M2HYB49baYOT1/h3osdbeHXnuIHKpt7V2POE7KDINhYFIFJFL9X5urb00ys9cQDXhKzo+ba39Q2PMpcCfWWt/L9LmWuBOa+1dxpj/Qng6iXFOD5bJnIQD41+stf878XskcnZpP+hMJFGMMXcQvj4ewpfteY0xO85o9hXgNeA44TA4FQ7RThOFAKy1PwV+Ohc1iySKwkDkQwXAU9bae4wxO6y1pz7sd1hrLzHGPEB4VOddhKcDKAI8hCcMe/JsG46cBpogPG8WQDnho4ETkecrgKuttdsTu0sisVEYiHxo8imcUmPMa9HaWGsfBB6MzCbaBvw1sJTwTLmTfTBrrrU2ZIwZtNZeAmCM+QqQZa39ZuT5Dk7vrxCZVwoDkeg6IhOYEeVUEcaYtYSPCE4CjxOeoG2VMWbXpGbPnLGaOugkZSkMRKKb6sgAY4wXeBC4F2ggPO1CB+EZUfdEHl9urd16xqoZk4KlHHAaYz4Reb4iseWLxEfjDESi67DWXnnq6OAM1wG/BN4EsNb+NeH5g34AlBCe6+b7xphbzlhvzFp7SeRU0X3Adyc9b5yrHRGJhS4tFYmITGj3d4SvFFrBhx/Qpx7XEb594NOR9puBTxE+RfQM8M+RK4cwxqwEfgt8zFp7ILJsjPCskwClhL+MnboV4XJgo7X21GR6IvNKp4lEPuQB/t1ae0+0H0auJpp8T4FcwnP2fwr45akgALDW7jPhe9DWAAciVxMFrLVrptj2jjO2LTKvdGQgIiLqMxAREYWBiIigMBARERQGIiKCwkBERFAYiIgICgMREQH+P0f7jH9jA/4AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['面积亩'])\n",
    "plt.savefig(r'G:\\python林业资源\\出图/sns直方图.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"地类\", y=\"面积亩\", hue=\"起源\", data=df)\n",
    "plt.savefig(r'G:\\python林业资源\\出图/sns箱线图.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[(df['平均胸径']!=0)&(df['平均树高']!=0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nMTLODTwBrDrzcFYunc+Sj72DZMIiNSWpN3+lKqBPAKpArfLQFNvPPt0prun9ALZtYTvQPzAEUPHyjbZjMTxYmOnzgiU9pDoTuNr2o1RZ9AlAFahZNtAS+/HxAb9gaOZJx7w1NJdPsSePYTf8SWVYb/5KlU0LAFWgVuPlw/bTu6SH7//iYZb13U7v6o1sHRhmZqqDvuUL2H+vVNEnjzCuV2xpRW3+Uapc2gTUYia6klT+eHksC3y/6D5KHWvsuHub7//iYe597BVg1EpjrlcwGzir1ExdJyQfUPB5G20DUqo8+gTQQvLX083WsqvJeOm6wSSqZMLG9XxGMvueyLFcz8vd/LPya/iVPnl0ONAb8oQxKWnlFqKJcspnpaJAnwBaSK3W8901hHNj0Q7Z8Y41ehjoyqXzS9bwK52pmx522SPVQd/yhbi+j2NZTEpabHl9py6lqFSZtHrUQkqN4Cklf/lG37HxnWLLN5Z/rNEFxNoNT47J8TO6hl9pBsz0sIvluuw1cyqW67Iz7elSikpVQJ8AWkg1GS+LTvwaZynF8Y41uoDIrtx1+fIFeHXKxaNLKSpVGX0CaCHVjOAp1pQTtpRi/lDQ8Y4VNgy0f2AoyMUzTg1/9BNJue34upSiUpXRilELqSbjZbFac3YpxWL5dcY7VrWLy08kFUUtFrRXqp1oAdBiKs14WawpJ+nYXLniKIZ2jhTc3POHfvqZ41i+P+ZY1aZfrqQje3TSOkBTPitVgboXACKSBNYB3zTG3CkiJwN/DQwDDwJfNsbokkxNUqzW7BAs7L55aCB3c6+0dl5N+uVy2/FLxWJlhrFqymelSqtrH4CIJAhu/nMyP3cCpwDvM8YcCSwE3lXPGJIdDr7jZNqTHZIdTj0PFzv5NfXv9x5L3/IFRW/o1SaKq6RNv1g7fsIu3IfL+COVGqXaPota70OpSjWiCWgZcCmAMWYIOBFyhcN04JWiW05QssNhy8BwQZ753iU97JHqIK1JY3LKralXM8qm0qeGsCeSC5f0MJQeYdU19+Reu+SsIyIx4qcW6bNrlYJbqUrVtZphjBkxxrxQ5O2vAbcaY16u1/GHXXI3fwhuEH2aMKxq1YyyqfSpIeyJZHqqI3fzz+7jpS3bIjHipxbps2uVglupSjWlE1hEPgQcDxxX6bYzZ04r+l53d6rg55e3bA+vJfo+e436bCONjjPK8mP1PJ+LzpjPpT+4N1dTveiM+ewxYwp2kRvvpv7B0N8BllX2eQjbx9oNT3LB53u4/If3lR1LPVTz/bKve57P69t3MrTTZemiudx8x1O5xXIqPUf1ENfrNMqiFmfDCwAROYKgSeh4Y8zOSrffunUbXsjM1u7uFJs3DxS85jhO+GQlyxrz2UYJizOqwmKd3pkYM8pm69ZtxXdSJMkbvl/+eQjZR//AEDOmdYxJWlcylnqo8Ptlz2lYs895J8/j+vXBimkVn6Mai/t1GkXNitO2raIV52b0NP0rsBuwTkTuFpFj63WgYgnDtB+4epWma6hFeuli+7DcIIZZXVPKiqUeqv1+Yc0+V9/0ECcd89aqU3ArVSnL92MzAnN/4JlKngAg6AgedoNslI5t0+HQ1A7guNRWoHaxTjRF9Xj7aPY5reT7ZWMdsSyW9d0+5v3v9x4bifkLzT6nlYhLrBF4Angz8Gz+ey1fyUgPu3kjXFzS2gFcM+Xe+KqZD1CPfdRLNbGVyqVkuV6kvp9qXTrYOIaiMGa8VmsPFNt3s79fvdVq5TWlJqLk9SYiXcCwMWZ7g+JR44jKmPFarT0wWlS+X71VmypDqVoar2p1FvCB/BdE5CUReVpEnsn8/yf1C0+NVsmY8XrWpKtde2A87TQmvtIOdaVqbbwnzjSQFpG3EyRUfAn4E3B05n0H+JWIJIwxrfg3Gjm1yJVTixtNNWsPlENz+ivVOOVUCS1gI8HwzfmADxwIfAp4H3Cc3vwbp9zZuPWuSderDVtz+levHfpOVG2N9/earcr9AbieoDCwgI8D3cC7jDG/ql94arRyc97XuyZdrzZszelfnXbpO1G1VfReICLvBo4FTJGP/H9gTc0jUiWVe+OtVxNNuesBVKuenaPlDlutxbyFRqtXp7xqbaUqgwuAPdn1FDCaX+I9VUfljDuvR026UbXMeoz5Lzf2uNakte9EVaNoI6Ex5rvADQRNPmE3+mX1CkpNXCV5/ssV5xE65cYe1++ofSeqGuX2Eu1H0O4PQWFwK/AAcHk9glITk+0M3JkZkjkpO7t0gjXYSoZ+Rq1DsnjsMGJZeI6N3eHglfick/ke+d8rWHCo+d9TJ5apapRzffjAZQQtB09kXnsUeAxARGxjTHSfjdtMPZswyu1XiGIzSrHYX9w8wMXX3susrsmsWDyPWV1Tin5u9u5TSKc9Lst8r/kHzWbxcW8vWHCoWd9TJ5apaoxXXUkASWPM940x1xljXgQOAJ4G/pj5//N1jlFVoJ5NGOXWMqPYjBIW+4rF81i74clcjFetfYhX39gx5nPnnRx87pVXB3M3f4BjD9tvzIJDzfyeOrFMVWq8J4AbgcH8F4wx+9QvHDVR9ewMLLeWGcUOydGx27bFP1z/QG4BlmyMngepqUkuX76Azf07GBhM53L0d3YkCr5Xakoyct9TqUqUfAIwxjwPvCoi729QPGqC6t0ZWE4ts9Edkp7nl9UOXxA7waIyo2McGh4BPxj58K0bH+TyNfflComh4ZGC7zUwmB73e47uMwhLZa5UsxRdD0BEjidIBWEB1wGfL7KPp40xz9YlukL7U8V6AFFTizhLjVOvZft7tbE2sg/AcWxeHxopWKaynGM5js1rg+lck062SagrNYmpyWDFoPzvMP+g2SxdNBfP83lpyzbWbniSGamOon0AAL5j8drAcMF5uOiM+UzvTIwZehq1eQdx+XuC+MQaxfUAShUAPyDo+E0DJwE3sWsm8DRggOAJ4uPGmH3rFHu+/dECoKyba61uKBOJtVE3Nd+x6V29cUynbd/yBVjjHM9xbFwsRjwf24aEY2G5/pjziAUD28ee8xmpDhxg2PULvicEhUf/wBDfu/nhkrFFscMc4vP3BPGJNYoFQKl5AGcYY840xiwHXjbGnAf8Gng/sNEY8yVjzDnAt0REF1lskHI6WKPQGdioGCaSldR1PXBdEr6H7Xp4w25BnNnvgB9+zl3XDxYcGvU9s7+j0X0GYbEV+336jhWJ4aWqtY23HoAD7EEw7h/gIOATgCMi3wG+ZIz5f/UNUeWLYgdrM9Ur5UW+Ss959vPZPoJSsYXtuyvVOabpKApPBar1jFeteBPBcM8Pi8hdQAr4HPBjYJ0xRnu0GqzVZnxOdMJYArjojPljhqZ21LAGXek5z37+5jue4ryT5xXEdtEZ8wvTdofse/Fxb4vcMFrVmsqpND5rjHmPiLwTuAV4kqBvQFcJa4JWypZZi/Zv1/XYb8/dCoamdjgWW2tYg670nOd//vr1j3P2SQez9x7TSDo2M2dMZuvWbSX3vfce0/QpTzXEeNeTBbya+fcy4HiCZqDngTUi8kNtAmqsVprxWasMlnYm1UU2edwwdk0zY1Z6zot/3sUe9dQQ9lmL+jdrKQXjzwN4FjhORB7IdAJ/BfgwQUqIoxnVo6waIwqdvLVQr2Ul67HfSs95JZ8f/VkHX/P6qIYotR7AEMESkEPAASLyPDCV4Ing/szHkiLyXWPMXnWPVMVKOcNAy+nArWY4aSUdw1Ecg++6HjNTHfQtX4jreTi2TYcD6eG4NfKpqCtaABhjOgFEJAE8BJwGnAB8GvhP4DJjzGsNiFHFTLlt+8Xa1rGC8f3VtuV3OBa9S3oKJmj1ZjqG027h+P7+kPH9zR5t4zh2TfswlCqmnKERHnCOMeZ3xphLgHkEq4TplahClZsMLr/9+5reD3DpF4/gxg1P8IVLb6N39Ua2Dgxz44YnKh4NM+z6rN3wBEsXzaVv+QKWLprL2g1PMOz6ucKpd/VG/vTCG5EcbRPFZHqqNZXTrDgFuCf7Q2YB+GvrFpEK1aymimqOm22DlzldnHTMW0lNSTIwmCasBza7+pft2PSu/vWYm97SRXO597FXcp8vZzSM6/nc+9grBdsBfOHj7wTbyt1co5rMTed6qEYp53p6imDiV4JgbYBseogdBP0B3zTG/Fv9QlTNShfgeX5Vx3Vsi/kHzeaEIw/g6pseKmvbYje96dM6Cl4rZzRMqT6A/OOUM1GrGRoxuU0pKHNFMGPMLGPM7saYmcDhBCuEvRO4FOgtta2IJEVkvYgcnfl5XxH5Tea/5RMLvz00q0ng9e07qzpugqC2nb35l7NtsclWe8zoZOXS+fQtX8DKpfNZdebh49Za8nP/y5wuVi6dzyVnHYGFRUfSzh0nbKJW/iQyP2HjOw6uZYHjYHc4DUnNoKt7qUYpd0UwAETkIOAcwBhjrhKRR6H4/SDz1LAOmJP38rXAJcB/AL8SkX/PpJ1WRTSrSSA94lV1XNf1sCyrom3DOoRXnXk42wbTuYRqs7omc+GSHkiWTj2V7Vv45pcWjkmpcOGSHladeTirrrkH83w/t971Jy79YlA42Da5jucbNzwx5glmxeJ5/PiXj9M/MFTXJ7BWmuuhoq1oNUZE/kZE9s37+evAPwEXAZ8VkXcbY542xswf5xjLgN9m9uEA7zbGrM+kkdhAMJ9AldCs9A/JhF31ce0KYw5bxL4zmeCyHxY+gVxW5pOP63q4rj/mCeayNffRmUzkjrPsxLlMsi0cPxiLP5zZ5tjD9hvzBHPV2oc46Zi3NuQJrFXmeqhoC63Iicg0gjxA64HZIvI14HdAnzEmLSLnAf8iIu8xxrxRbOeZDuMXRCT70hTgxbyPvAbsXUnAmbSmobq7U5XsqmkqjdPzfC46Y35BzvuLzpjPHjOmjJlZWksTOW4tYt7UPxj6FIFljTmHYee02Pa+BbO6ppY8ZrEO4lQm13+xOMrRqtdpM8Ul1qjFGVoAGGO2AecBiMjbgDOBcwkygT5gjPmNiKwEhis83iAwKe/nFKFjQ4pr1/UApncmxjQJ5OeUqYfu7tSEjjvhmB07tDMU3y84h0XPaZnbh21TrIN4YDBd3n6KaPXrtBniEmsE1gMY+954GxtjniRow18IPAIgIp8nWCNgqNS2IftyCZaYzDYtHQI8U8k+2lUtmgSqybw5keNONOaJdoZWs312m9vvf25MB/GKxfO4+Y6ntFNWtYxSqSA+xa4a/hEEN2s/05wzBzgKuLOKY14J/ExEfg0cBpxVxT5UhaK68lQpE+0MrWb77DbLTpwLFvQtX4iX2dZ24KunHqKdsqpllKrEnESQB8gnWAJyEYXNNceKSNIYkx7vIMaYJXn/XiciTxIUKKsyzU2qzmqVebPRshPFspk+K82GU8322W2yHAAXPLf6OJSKolK5gBZn/y0iC4Clxhgv77VfAJ8Cbqj0oMaYx4HHK91OVU9nlyqlRit3Nsunszd/EXl/5rVvAHfVJSo1IWFt/a22kphSauJK9QFYBLN9HzXG/C7vre8BbzfG3BO6oWqqYm39M1MdLbOSmFKqNko9/dvAT4AbRGQ+sMwYswVdCjLSSrX16+xSpVS+ok1AmSGbaeBqguGfD4rIUWiFMdJKtfXHbXbpRBeMV0qVNl7/n2eM2QGsFJH1BAWCirBWySQZx2GrSsXNeAXAPiLy93k/W8Deea/ZQIcx5m/qEp2qWAK4cEkPl41KgBa3tv5sU1ZXqpOli+aSmpKkf2CIGamOeH0RpSJs3CcAxrb5+3mv2ZTIBqqaI5m0Ofukg+nsSDA0PEIyGb+mE9fz6Up1ctpHDix7TQGlVGXGKwD+Yoy5AkBETgDuBj6ZfU1Fzwiw6pp7xjQBRX3C12iObbH4uLeFrikQt++iVFSNVwD4ImIDVxEsCH8KeesDqOgp1Qk8qQHLStZq6coE8Oa9d2Pl0vfi2OB68PM7n+K2+/885rv0DwzhOHbBcZq1hKZScVJqHoANJIGPAt3AQcaY7XmpnVUEFesE7kja9I9aHKXWzSm17Li1HYstA8P05e3r/NMPIzUlOe530Q5kpcozXuPw3xljbjXGLDbGZNv9wxOpq0golgHTc+u/rGQtl64cdsnd/LP7+saP7uejC98y7ndp1hKaSsVNqVxAHvDvIW+dW79w1EQVy4C506tueceKjl3DfENukXg9DzxKfxfNe6RUeSoeHmKM+a96BKJqJ2zCVyNyAdXyGI5dfDnK8Y6jeY+UKk/8xgeqqkx0cZVGH6PDgd5R++pd0kOHM/5xGvFdlWoFlu/HZlDP/sAz7bIkZLWjWEptV+k+qzmntRx9k+xwGHaD5iDHtulwID3sjjlO56QEftqd0CigRo0aarXrNAriEmsEloR8M/Bs/ntaKYqgakexjLfdRBdXKUctj5EedvP25ZLO21n+cbpSnWP+sCqJQ0cNqXalTUARVO0olmq3a/ekazpqSLUrfQKIoGpHsVSzXanab7vQUUOqXbVXVS8mqh3FUs12WvvVUUOqfWkBEEHVjmKpZrtStd92oaOGVLvSazyCik3mGq9DsprtSq4f0CaqPd9KxZ0+AURU6GSuMjprS636Fba91n4DcVstTalaaLe/89ia6FDFUttr7Vep9qRPADEx0c7aUttr7be22n1YrYoPfQKIiYkOVdShjo2hk8pUnGjVJCYmOlRRhzo2hg6rVXHS8AJARPYWkf8UkTsz/+3b6BjiKKyz9sIlPTiOVVZTg3b2NkapJy3HsekfGNKmIRUZzfj7/zLwU2PM9SJyCnA+cE4T4oiV0UMVE7bNUHqEv/n23WU1NehQx8YYf0W2jdo0pCKjGVWQTcBcEZkCzAeeaEIMsZTfWevjFyz+Xk5Tg3b21l8zV2RTqlLNeAL4F+A64DxgL+DblWycSWsaqrs7NaHAGqUWcW7qHwxtasCyanoe2umc1sqMGT5XrjiK9IhHMmEzfeoktry+oyG/r1qKalxh4hJr1OJsRgGwEvi/xpjfi8gM4FbgyHI3bpf1AMbl2KFNDfh+zc5D253TGrOAkRGXrUPphvy+aimq5zRMXGKNwHoAY99rcCwA04FDMv8+BmifpDM1pJ268aK/LxVFzbj+LgF+ICLfBZ4DljYhhtjTTt14yf6+rlxxFEM7R/T3pSKh4QWAMeb3wKGNPm4rasQKX6p2XNeje/epbB4a0N+XigQdiKyUUm1KC4AY0lwzSqla0D6omNFcM0qpWtGqY8xorhmlVK1oARAz1SzhqE1GSqkw2gQUMyWXcHTHFgLaZKSUKkargjFT6YQibTJSShWjTwAxU+kEMF0IRilVjD4BxFAlWT11IRilVDFaALQ4zUGjlCpG7wMtLgo5gxzHDhafDzl+qfeUUvWlBUAbaGbOoFKjkAAdoaRUE2kTkKqrUqOQdISSUs2lTwCqrsabuKYjlJRqHn0CUHVVahSSjlBSqrm0AFA1USzdRKlRSDpCSanm0r81NWHjpZsoNQqp2SOUlGpn+gSgJmy8ztxSE9cqmdSmlKotLQDUhFWToVQp1XxaAKgJ085cpeJJCwA1YdqZq1Q86d+omrAopJtQSlVOC4BxaK6a8jQz3YRSqjpaAJSgq2kppVqZ9gGUUG2umrBJUdnXNvUP6rq8SqlI0CeAEqpZTSvsqWHVmYeTTntcpk8SSqkI0WpoCdUMbwx7anjl1cHczT/7mma9VEo1W9MKABFZIiLXNuv45ahmeGPYU0NnRyL0ScLzCM2fU41iuXiUUqqYpjQBichbgK8CC5px/HJVM7wx+9SQf8MfGh4Z89qsrsn4+PSu/vWEm4W0s1opVY2GVxNFxAZ+AjwGfE5EZjY6hkpUmqsm7Klh9u5TuDDkSeK6Wx6tSbOQLqyilKpGM54ATgM84K+BA4A7RGSeMaasqurMmdOKvtfdnapJgBM1Y4bPlSuOIj3ikUzYTJ86CaDgNc/zufexVwq229S/Ayyr4u+xqX8wtImpmn2NFpVzOp64xAnxiTUucUJ8Yo1anM0oAA4DrjXGvAS8JCI7CQqCp8rZeOvWbXghSca6u1Ns3jxQ00AnygJGRly2DqVzr83KxOk7dmizEL5f+feo5b7yRPGcholLnBCfWOMSJ8Qn1mbFadtW0YpzM3oK/wAcCCAis4F9gReaEEdT1TJ/jubiUUpVoxn3iB8C14jIr4Hdga8ZY3aMs03LqWX+nKo6qzXFhVJtr+EFQOZm/9lGHzeKapk/p5J96aghpRToRLC2pKOGlFKgBUBb0hW8lFKgBUBb0hW8lFKgBUBb0lFDSinQbKBtSVfwUkqBFgAtp9zhnbqCl1JKC4AWosM7lVKV0D6AFqLDO5VSldACoIXo8E6lVCW0AGghOrxTKVUJLQBaiA7vVEpVQu8NLUSHdyqlKqEFQIvR4Z1KqXJpE5BSSrUpLQCq4Dg2vmMzYln4jo3j6GlUSsWPNgFVSCdbKaVahVZdK6STrZRSrUILgArpZCulVKvQAqBCOtlKKdUqtACokE62Ukq1Cr1vVUgnWymlWoUWAFXQyVZKqVagTUBKKdWmtABQSqk2pQWAUkq1KS0AlFKqTcWpE9gBsEuMty/1XpTEJU6IT6xxiRPiE2tc4oT4xNqMOPOO6Yx+z/L92MxgXQjc1ewglFIqpo4E7s5/IU4FwCTgMOBl0JGXSilVJgfYC7gf2Jn/RpwKAKWUUjWkncBKKdWmtABQSqk2pQWAUkq1KS0AlFKqTWkBoJRSbUoLAKWUalOxmQksIp8BluW91AMcYIx5OeSzfwA2ZX78b2PMygaEGCsikgTWAd80xtwpIicDfw0MAw8CXzbGhI4RFpHTgfOBVzIvfd4Y80wDwo60kHO6AejIvD0LuMsYc1aRbfWc5hGRFHADwfyfLuBMQNBrtKZiUwAYY24guCAQkR6CX37Yzf9NwB+NMR9vcIhhN4B9gX/JvH29MWZ1iW3L/mwN4kxk4pyT+bkTOAV4nzFmSEQeAN4F/K7ILo4ETjXGPFivGPNiHX1Oy/7DbuY5BTDGHJf3/i3At0vsoiHntMiNdSvRu05PA35ijLlJRE4AvgEMEc1rNOycvosIXqejxaYAGOVy4AtF3jsKeJeI3E3QxHWeMea39Q4o7AYAXAtcAvwH8CsR+XdjzPNFdlHJZ2thGXApgDFmCDgRct9jOrsu3DBHAgeIyG7A3caYFfUIsMg5reQPu2nnNJ+IHAO8bIx5tMS2DTmnjL2xrgImE7HrdNRNcBbwtDFmOUTrGs0IO6dbiO51mhO7PgARORJ43hjzXJGP/AE43hizkKAE/oeGBRfcAH4LICIO8G5jzPrMY+oG4OiwjSr5bC0YY0aMMS8UeftrwK1hT1cAImIBXweOAd4DHCQi76tPpEDeOc04EvhHEXlARK4qtlHEzun5wBXFtm3kOTXGrDbG3JT5cRZBapVIXqeZY+4BfJXgCSArUtdoyDl9iYhep6PFrgAAvgT8U4n3jTHm8cy/fwe8o+4REXoDmAK8mPfza8DeRTav5LN1IyIfAo4nuGGFylyktxhj/My/H6ZO53j0Oa3wDzsq5/QdQNoY83SxzzTynObFlX9jjeR1mmn+uxE4P1sjjto1Oire7Dm9gphcp7EqAERkOvBOY8x9JT62WkQ+mPn3JymsPTbSIEGbYFYKKJYLtpLP1oWIHEHQfPEJY8zOEp/bH/hPEXFEZBrwIeCBRsRY4R92089pxunA2lIfaPQ5zb+xAi8Qwes0UzO+AVhnjLkl81pkr9FRhdVzxOQ6jVUBQPCLzKWEFpELM+2r+VYBF4vII8CngOWNC28XY4wLvJrp4AE4BAjtBKrks3X0r8BuwDoRuVtEjhWRfUTkx6NifRZYDzwObAS+N06BXDOV/GFH5JwCnADclv2h2ed09I01wtfpGcDHgMWZ6/GnRPQaHX1O43SdajbQGhORNcCazIiVRcBFwK+BDxP8cqcDfcaYz43absxnjTHbGhl7VI06p39LMABgB3CNMeY7IrIPek7LIiJnAlez64b0HMEIFL1Oq1TknD5MDK5TLQDqTEQOJPiDWm+M6a/VZ1V59JyWR6/T5mrWOdUCQCml2lTc+gCUUkrViBYASinVprQAUKoCIpLKzEdQKva0AFBtJTM0zynxfkJE7Lyfb5IgUV7W5cDfVnC8i0Vk4Tif+VJmuKBSDRXXXEBKVesjwCUiMpL5eU+CitBLBBNwOoDPAr/PvD9EkH0y6/3AR8N2LCK3AXsQDKH8EdCX2deVxYIRkXOBDwD/KiK/Ad4A/EwsU4AzjDEPVfwtlSqDFgCqrRhjbs3U8H9pjBkRkfOBacaYi0RkCrACyE/a5mf+Q0SEYEbnvcE/c95mjHmDoLA4PvPf3sCnARe4Ne/z78789xzBpMV5wLmAbYyZm/2QiHwHGNSbv6onLQBUO/oM8EGCG2++vwXeZYxxRWQewQSpWcAHReRUgpt2rzEml9hNRLZQ+IRwCHAAsJMgL8wJBOkWPmuM+WcRuTPz+d0Injw+SZBC4HaCNCYzgX8GjiDII6NU3WgfgGpHXwA+npmBCYCIHJ55Pbtgy++BA4GfExQUXwVOBoyI5C8wlCC42WedCMwnqOU/CvwdQU1/et5nPGPM68aYv8u8/yJwi4isIigIrgcWE6Q8uEhE8nPFKFUzWgCotpOZZr+YvLxSQJqglv5K5jOeMSbbT+ATrDD3CYIcM8eJSLa5JpvwK+vrBHlhfkvQvHMJcA6wRUQuz35IRDpE5Grgy8DPCJqLNgHzjTHrjDH/Q7C2xU52rSqmVE1pE5BqG5nEgd8hyMCYle0EPj7zGRuYBpxtjLk9+6HMojn3Zz7zceBSEbmSoI0/yyK4+XcDPyVYFORggpv4vhQ2Fb0N+EvmcxAsBvIz4CwR8fP2t94YMzChL65UEVoAqLZhjLmDUWl58zuBS2z6bhGZaYy5TkTeS7C04w3A8wRPDlkzCZqRPkzwt+UT1P4/DfyQYOWnbCyPAo+KSHZtC0PwxLAT+C9gKfA0kKzu2yo1Pi0AlAqRudGfDxxK0C7/PyLyGeDvgZONMQ+IyGSCbI9ZuxtjHhORw9j1ZLEIeB24mOApIJQxxiMYRUR2xFDmtaJ575WaKC0AVLuzCe8Le5Eg//yns4uPZIaJrjfGvCYiBwAHAa9m3juQYLQP7Pq7eoVg9TqLoBP47pDjJICEiEwKW+RERDoh1wSlVE1pAaDa3SRCOlkzSxD+ZNRrg+zqP7iRoHmmL/PzXwH/ISKfI6jtf9kYk1vHVkTeTDDHYC6Q36afBN4EPCAi+Tf5tZkngQTBKKJfVPXtlCpB00ErVQOZJQGnAJ1AlzHmiVHvTwIuAH5ujPld4yNUaiwtAJRSqk3pPACllGpTWgAopVSb0gJAKaXalBYASinVprQAUEqpNvW/eV1ISeqaXzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"平均胸径\", y=\"平均树高\", data=data)\n",
    "plt.savefig(r'G:\\python林业资源\\出图/sns散点图.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
